Abstract: To address the problems that it is difficult to classify igneous rock lithology in igneous rock reservoirs and the lithology identification accuracy is greatly affected by the number of slice identification samples,the correlation between different logging curves and igneous rock lithology was analyzed by random forest (RF) algorithm,and then igneous rock lithology was classified by the the K-nearest neighbor (KNN) algorithm according to the slice sample identification.The study results were applied to the Permian igneous rock formation in Western Sichuan,and the results showed that the correlation between logging curves and lithology was decreased in order of GR, R_t, DEN,CNL and AC.The igneous rock lithology was classified with the KNN algorithm, and the value of k was controlled by two factors: the number of classifications and the number of training samples.When there were less samples,the effect of the latter was greater than that of the former.When k was 3, the backcasting accuracy of KNN algorithm was 87.5% for 24 igneous rock training samples (5 types of lithology),and the testing accuracy was 92.5% for 14 igneous rock samples (5 types of lithology).In the classification of igneous rock lithology with comparison of charts,there was less man-made influence on the KNN algorithm and the parameter adjustment was simple.This study provides an important guide to the classification of igneous rock lithology with small samples.

Key words: igneous rock reservoir, lithology classification, slice identification, KNN, random forest